You can download the raw source code for these lecture notes here.

Course Meeting Plan

Wednesday · 12 March · Lecture

  • Lecture: Onset detection (15 mins)
  • Breakout: Energy or spectrum? (15 mins)
  • Discussion: Breakout findings (5 mins)
  • Demo: Beat tracking (15 mins)
  • Lecture: Tempo estimation (15 mins)
  • Discussion: Preferred tempo (10 mins)
  • Portfolio critiques (15 min)

Wednesday · 12 March · Novelty Functions

  • Demo: Novelty functions in Spotify (15 mins)
  • Breakout: Novelty functions (20 mins)
  • Discussion: Breakout findings (10 mins)
  • Demo: Tempograms in Spotify (15 mins)
  • Breakout: Tempograms (20 mins)
  • Discussion: Breakout findings (10 mins)

Set-up

Again, you will want to download a new version of compmus.R this week. You should also check make sure that you have the signal package installed, which is necessary for generating tempograms. You do not need to load it explicitly, but the new functions in compmus.R will complain if it is not installed. Use Install Packages (under the Tools menu) in RStudio to install it.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
source("compmus.R")

Breakout 1: Energy or Spectrum?

Look at one (or more) of the self-similarity matrices from somebody’s portfolio in your group. Discuss what you think a spectrum-based novelty function would look like for this track. Listen to (some of) the track and also discussion what you think an energy-based novelty function would look like. Which one do you think would be most useful for beat tracking, and why?

Breakout 2: Novelty Functions

We can compute an energy-based novelty function based on Essentia’s loudness estimates. Try the compmus_energy_novelty() function on several files in the class corpus. How well do they seem to work?

"features/ahram-j-1.json" |>
  compmus_energy_novelty() |> 
  ggplot(aes(t, novelty)) +
  geom_line() +
  theme_minimal() +
  labs(x = "Time (s)", y = "Energy Novelty")

We can use similar approaches for cepstrograms to get an approximation to spectral novelty, with the compmus_spectral_novelty() function. Do you find the novelty functions using this approximation to be more or less helpful than the energy novelty?

"features/ahram-j-1.json" |>
  compmus_spectral_novelty() |> 
  ggplot(aes(t, novelty)) +
  geom_line() +
  theme_minimal() +
  labs(x = "Time (s)", y = "Spectral Novelty")

Breakout 3: Tempograms

The new compmus_tempogram() function generates a Fourier-based tempogram from Essentia’s estimates of beat location and beat loudness, ready to plot with geom_raster(). Be warned that computing tempograms can be slow! Try increasing the window size and hop size if you find that you are waiting too long.

"features/ahram-j-1.json" |>
  compmus_tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) |>
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()
## `summarise()` has grouped output by 'time'. You can override using the
## `.groups` argument.

The textbook notes that Fourier-based tempograms tend to pick up strongly on tempo harmonics. Wrapping into a cyclic tempogram can be more informative.

"features/ahram-j-1.json" |>
  compmus_tempogram(window_size = 8, hop_size = 1, cyclic = TRUE) |>
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()
## `summarise()` has grouped output by 'time'. You can override using the
## `.groups` argument.

Instructions

Return to the track you discussed in Breakout 1 (or choose a new track that somebody in your group loves). Compute regular and cyclic tempograms for this track. - How well do they work? - Do you see more tempo harmonics or more tempo sub-harmonics? Is that what you expected? Why? - Try other tracks as time permits, and be prepared to share your most interesting tempogram with the class.